Overview

Dataset statistics

Number of variables18
Number of observations17464
Missing cells17472
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory144.0 B

Variable types

NUM13
CAT3
BOOL1
UNSUPPORTED1

Reproduction

Analysis started2020-08-08 22:44:19.563083
Analysis finished2020-08-08 22:44:56.971269
Duration37.41 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

username has a high cardinality: 10490 distinct values High cardinality
stopwords has a high cardinality: 14801 distinct values High cardinality
clean_text has a high cardinality: 16317 distinct values High cardinality
likes_count is highly correlated with retweets_countHigh correlation
retweets_count is highly correlated with likes_countHigh correlation
positive is highly correlated with Unnamed: 0 and 2 other fieldsHigh correlation
Unnamed: 0 is highly correlated with positiveHigh correlation
date is highly correlated with positive and 1 other fieldsHigh correlation
death is highly correlated with date and 1 other fieldsHigh correlation
geo has 17464 (100.0%) missing values Missing
clean_text is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
geo is an unsupported type, check if it needs cleaning or further analysis Unsupported
replies_count has 10882 (62.3%) zeros Zeros
retweets_count has 11418 (65.4%) zeros Zeros
likes_count has 8671 (49.7%) zeros Zeros
stopwords_count has 682 (3.9%) zeros Zeros
Sentiment has 4437 (25.4%) zeros Zeros

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct count17464
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8731.5
Minimum0
Maximum17463
Zeros1
Zeros (%)< 0.1%
Memory size136.4 KiB
2020-08-08T18:44:57.105698image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile873.15
Q14365.75
median8731.5
Q313097.25
95-th percentile16589.85
Maximum17463
Range17463
Interquartile range (IQR)8731.5

Descriptive statistics

Standard deviation5041.566886
Coefficient of variation (CV)0.577399861
Kurtosis-1.2
Mean8731.5
Median Absolute Deviation (MAD)4366
Skewness0
Sum152486916
Variance25417396.67
2020-08-08T18:44:57.271057image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
170371< 0.1%
 
149781< 0.1%
 
129311< 0.1%
 
26921< 0.1%
 
6451< 0.1%
 
67901< 0.1%
 
47431< 0.1%
 
108961< 0.1%
 
4851< 0.1%
 
Other values (17454)1745499.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
174631< 0.1%
 
174621< 0.1%
 
174611< 0.1%
 
174601< 0.1%
 
174591< 0.1%
 

date
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count198
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5936496622995878e+18
Minimum1579651200000000000
Maximum1596672000000000000
Zeros0
Zeros (%)0.0%
Memory size136.4 KiB
2020-08-08T18:44:57.409671image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1.5796512e+18
5-th percentile1.5848352e+18
Q11.5937344e+18
median1.5945984e+18
Q31.5955488e+18
95-th percentile1.5964128e+18
Maximum1.596672e+18
Range1.70208e+16
Interquartile range (IQR)1.8144e+15

Descriptive statistics

Standard deviation3.417583284e+15
Coefficient of variation (CV)0.002144500994
Kurtosis4.833024516
Mean1.593649662e+18
Median Absolute Deviation (MAD)8.64e+14
Skewness-2.298792455
Sum-4.639104828e+18
Variance1.167987551e+31
2020-08-08T18:44:57.576577image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.5945984e+185913.4%
 
1.5939936e+185483.1%
 
1.5941664e+185142.9%
 
1.59408e+184862.8%
 
1.594944e+184842.8%
 
1.5948576e+184552.6%
 
1.5943392e+184502.6%
 
1.593648e+184492.6%
 
1.5960672e+184302.5%
 
1.5942528e+184242.4%
 
Other values (188)1263372.3%
 
ValueCountFrequency (%) 
1.5796512e+18170.1%
 
1.5797376e+18250.1%
 
1.579824e+18160.1%
 
1.5799104e+18160.1%
 
1.5799968e+18180.1%
 
ValueCountFrequency (%) 
1.596672e+182251.3%
 
1.5965856e+182751.6%
 
1.5964992e+183351.9%
 
1.5964128e+183672.1%
 
1.5963264e+183321.9%
 

username
Categorical

HIGH CARDINALITY

Distinct count10490
Unique (%)60.1%
Missing0
Missing (%)0.0%
Memory size136.4 KiB
realdonaldtrump
 
2524
washingtonpost
 
576
nytimes
 
267
freddiesirmans
 
160
bornwildm
 
116
Other values (10485)
13821
ValueCountFrequency (%) 
realdonaldtrump252414.5%
 
washingtonpost5763.3%
 
nytimes2671.5%
 
freddiesirmans1600.9%
 
bornwildm1160.7%
 
democratboricua940.5%
 
nygovcuomo830.5%
 
davidhamer_1951590.3%
 
sudiptamalakar4460.3%
 
ykhalim450.3%
 
Other values (10480)1349477.3%
 
2020-08-08T18:44:57.873999image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length15
Median length12
Mean length11.8397847
Min length2

replies_count
Real number (ℝ≥0)

ZEROS

Distinct count2672
Unique (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2756.2297297297296
Minimum0
Maximum193481
Zeros10882
Zeros (%)62.3%
Memory size136.4 KiB
2020-08-08T18:44:58.008722image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile18507.55
Maximum193481
Range193481
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10462.96546
Coefficient of variation (CV)3.79611516
Kurtosis61.77067185
Mean2756.22973
Median Absolute Deviation (MAD)0
Skewness6.541899281
Sum48134796
Variance109473646.3
2020-08-08T18:44:58.143700image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01088262.3%
 
1220912.6%
 
24932.8%
 
31741.0%
 
4680.4%
 
5580.3%
 
6350.2%
 
7320.2%
 
13300.2%
 
12300.2%
 
Other values (2662)345319.8%
 
ValueCountFrequency (%) 
01088262.3%
 
1220912.6%
 
24932.8%
 
31741.0%
 
4680.4%
 
ValueCountFrequency (%) 
1934811< 0.1%
 
1911331< 0.1%
 
1877051< 0.1%
 
1717771< 0.1%
 
1692761< 0.1%
 

retweets_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count2891
Unique (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3547.869617498855
Minimum0
Maximum216656
Zeros11418
Zeros (%)65.4%
Memory size136.4 KiB
2020-08-08T18:44:58.290385image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile25920.85
Maximum216656
Range216656
Interquartile range (IQR)2

Descriptive statistics

Standard deviation10626.27552
Coefficient of variation (CV)2.995114439
Kurtosis29.21936164
Mean3547.869617
Median Absolute Deviation (MAD)0
Skewness4.387111108
Sum61959995
Variance112917731.4
2020-08-08T18:44:58.416051image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01141865.4%
 
113437.7%
 
24302.5%
 
32141.2%
 
41270.7%
 
5750.4%
 
6480.3%
 
7440.3%
 
8310.2%
 
10310.2%
 
Other values (2881)370321.2%
 
ValueCountFrequency (%) 
01141865.4%
 
113437.7%
 
24302.5%
 
32141.2%
 
41270.7%
 
ValueCountFrequency (%) 
2166561< 0.1%
 
1171441< 0.1%
 
1117531< 0.1%
 
1109001< 0.1%
 
1085471< 0.1%
 

likes_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct count3152
Unique (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16163.446518552451
Minimum0
Maximum808801
Zeros8671
Zeros (%)49.7%
Memory size136.4 KiB
2020-08-08T18:44:58.811522image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q37
95-th percentile115019.55
Maximum808801
Range808801
Interquartile range (IQR)7

Descriptive statistics

Standard deviation50353.45501
Coefficient of variation (CV)3.115267215
Kurtosis30.8175804
Mean16163.44652
Median Absolute Deviation (MAD)1
Skewness4.712530545
Sum282278430
Variance2535470432
2020-08-08T18:44:58.947501image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0867149.7%
 
1229813.2%
 
29435.4%
 
34682.7%
 
43001.7%
 
52001.1%
 
61480.8%
 
71130.6%
 
8830.5%
 
9800.5%
 
Other values (3142)416023.8%
 
ValueCountFrequency (%) 
0867149.7%
 
1229813.2%
 
29435.4%
 
34682.7%
 
43001.7%
 
ValueCountFrequency (%) 
8088011< 0.1%
 
7078041< 0.1%
 
6202981< 0.1%
 
5811561< 0.1%
 
5610441< 0.1%
 

video
Boolean

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size136.4 KiB
0
17085
1
 
379
ValueCountFrequency (%) 
01708597.8%
 
13792.2%
 

geo
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing17464
Missing (%)100.0%
Memory size136.6 KiB

positive
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count187
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3186199.8137883646
Minimum2
Maximum4852143
Zeros0
Zeros (%)0.0%
Memory size136.4 KiB
2020-08-08T18:44:59.094391image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile42169
Q12786467
median3350434
Q34093266
95-th percentile4694126
Maximum4852143
Range4852141
Interquartile range (IQR)1306799

Descriptive statistics

Standard deviation1232355.54
Coefficient of variation (CV)0.3867791136
Kurtosis0.8071931655
Mean3186199.814
Median Absolute Deviation (MAD)618190
Skewness-1.106819792
Sum5.564379355e+10
Variance1.518700176e+12
2020-08-08T18:44:59.224462image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
33504345913.4%
 
29285905483.1%
 
30425035142.9%
 
29803564862.8%
 
36268814842.8%
 
35496484552.6%
 
31679844502.6%
 
27322444492.6%
 
44678524302.5%
 
31013394242.4%
 
Other values (177)1263372.3%
 
ValueCountFrequency (%) 
21200.7%
 
3400.2%
 
48< 0.1%
 
68< 0.1%
 
7110.1%
 
ValueCountFrequency (%) 
48521432251.3%
 
47979592751.6%
 
47456943351.9%
 
46941263672.1%
 
46445653321.9%
 

death
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count162
Unique (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean118527.3047984425
Minimum2.0
Maximum151483.0
Zeros0
Zeros (%)0.0%
Memory size136.4 KiB
2020-08-08T18:44:59.358482image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile480
Q1122140
median127909
Q3137602
95-th percentile147631
Maximum151483
Range151481
Interquartile range (IQR)15462

Descriptive statistics

Standard deviation37236.70137
Coefficient of variation (CV)0.3141613777
Kurtosis4.405960671
Mean118527.3048
Median Absolute Deviation (MAD)7062
Skewness-2.3240636
Sum2069960851
Variance1386571929
2020-08-08T18:44:59.499823image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1279095913.4%
 
1228985483.1%
 
1246285142.9%
 
1238214862.8%
 
1314014842.8%
 
24792.7%
 
1304504552.6%
 
1263494502.6%
 
1215424492.6%
 
1441144302.5%
 
Other values (152)1257872.0%
 
ValueCountFrequency (%) 
24792.7%
 
4170.1%
 
590.1%
 
8200.1%
 
11280.2%
 
ValueCountFrequency (%) 
1514832251.3%
 
1502322751.6%
 
1488073351.9%
 
1476313672.1%
 
1471123321.9%
 

word_count
Real number (ℝ≥0)

Distinct count74
Unique (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.87597343105818
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Memory size136.4 KiB
2020-08-08T18:44:59.629596image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q127
median38
Q346
95-th percentile52
Maximum91
Range90
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.66368262
Coefficient of variation (CV)0.3529850596
Kurtosis-0.3203013052
Mean35.87597343
Median Absolute Deviation (MAD)9
Skewness-0.5747408274
Sum626538
Variance160.3688575
2020-08-08T18:44:59.753219image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
477014.0%
 
456713.8%
 
466553.8%
 
436543.7%
 
426443.7%
 
445943.4%
 
485903.4%
 
415533.2%
 
385403.1%
 
395393.1%
 
Other values (64)1132364.8%
 
ValueCountFrequency (%) 
14< 0.1%
 
2390.2%
 
3780.4%
 
41090.6%
 
5870.5%
 
ValueCountFrequency (%) 
911< 0.1%
 
901< 0.1%
 
861< 0.1%
 
831< 0.1%
 
811< 0.1%
 

avg_word_length
Real number (ℝ≥0)

Distinct count3807
Unique (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.864222972373408
Minimum3.0526315789473686
Maximum122.11111111111113
Zeros0
Zeros (%)0.0%
Memory size136.4 KiB
2020-08-08T18:44:59.880181image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3.052631579
5-th percentile4.086956522
Q14.634146341
median5.243902439
Q36.368421053
95-th percentile9.6
Maximum122.1111111
Range119.0584795
Interquartile range (IQR)1.734274711

Descriptive statistics

Standard deviation2.258274043
Coefficient of variation (CV)0.3850934819
Kurtosis414.1889093
Mean5.864222972
Median Absolute Deviation (MAD)0.743902439
Skewness10.3631277
Sum102412.79
Variance5.099801653
2020-08-08T18:45:00.007736image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
51691.0%
 
61320.8%
 
4.51010.6%
 
4.6880.5%
 
4830.5%
 
5.5810.5%
 
7730.4%
 
4.666666667690.4%
 
4.833333333690.4%
 
4.75670.4%
 
Other values (3797)1653294.7%
 
ValueCountFrequency (%) 
3.0526315791< 0.1%
 
3.1463414631< 0.1%
 
3.1538461541< 0.1%
 
3.21< 0.1%
 
3.2131147541< 0.1%
 
ValueCountFrequency (%) 
122.11111111< 0.1%
 
361< 0.1%
 
31.751< 0.1%
 
27.428571431< 0.1%
 
24.666666671< 0.1%
 

stopwords_count
Real number (ℝ≥0)

ZEROS

Distinct count36
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.272159871736143
Minimum0
Maximum36
Zeros682
Zeros (%)3.9%
Memory size136.4 KiB
2020-08-08T18:45:00.147264image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median13
Q317
95-th percentile23
Maximum36
Range36
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.596125496
Coefficient of variation (CV)0.5374869269
Kurtosis-0.6330147516
Mean12.27215987
Median Absolute Deviation (MAD)5
Skewness0.03804718459
Sum214321
Variance43.50887156
2020-08-08T18:45:00.286164image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1510025.7%
 
139865.6%
 
149575.5%
 
129425.4%
 
179325.3%
 
169075.2%
 
108624.9%
 
118374.8%
 
188064.6%
 
77764.4%
 
Other values (26)845748.4%
 
ValueCountFrequency (%) 
06823.9%
 
13071.8%
 
24322.5%
 
35193.0%
 
46113.5%
 
ValueCountFrequency (%) 
361< 0.1%
 
352< 0.1%
 
334< 0.1%
 
327< 0.1%
 
31100.1%
 

char_count
Real number (ℝ≥0)

Distinct count457
Unique (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean233.99559092991296
Minimum10
Maximum1110
Zeros0
Zeros (%)0.0%
Memory size136.4 KiB
2020-08-08T18:45:00.429363image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile93
Q1186
median255
Q3279
95-th percentile339
Maximum1110
Range1100
Interquartile range (IQR)93

Descriptive statistics

Standard deviation75.58938762
Coefficient of variation (CV)0.3230376578
Kurtosis1.16897423
Mean233.9955909
Median Absolute Deviation (MAD)40
Skewness-0.3282087694
Sum4086499
Variance5713.755521
2020-08-08T18:45:00.547489image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2807564.3%
 
2796673.8%
 
2784762.7%
 
2773602.1%
 
2763111.8%
 
2752541.5%
 
2742351.3%
 
2731881.1%
 
2711821.0%
 
2721811.0%
 
Other values (447)1385479.3%
 
ValueCountFrequency (%) 
105< 0.1%
 
111< 0.1%
 
12120.1%
 
133< 0.1%
 
163< 0.1%
 
ValueCountFrequency (%) 
11101< 0.1%
 
6021< 0.1%
 
6011< 0.1%
 
5951< 0.1%
 
5941< 0.1%
 

stopwords
Categorical

HIGH CARDINALITY

Distinct count14801
Unique (%)84.8%
Missing0
Missing (%)0.0%
Memory size136.4 KiB
[]
 
682
['and']
 
112
['of', 'of', 'and', 'of', 'and', 'in', 'below']
 
51
['in', 'for', 'and', 'on', 'on']
 
47
['you']
 
43
Other values (14796)
16529
ValueCountFrequency (%) 
[]6823.9%
 
['and']1120.6%
 
['of', 'of', 'and', 'of', 'and', 'in', 'below']510.3%
 
['in', 'for', 'and', 'on', 'on']470.3%
 
['you']430.2%
 
['to', 'and', 'for']340.2%
 
['for', 'to', 'on', 'all', 'the', 'by', 'to', 'for', 'and', 'in', 'to', 'and']340.2%
 
['into', 'than', 'and', 'is', 'more', 'the', 'of', 'the']320.2%
 
['of', 'at', 'is', 'than', 'and', 'is', 'at']310.2%
 
['of', 'so', 'is', 'than', 'and', 'is', 'at']280.2%
 
Other values (14791)1637093.7%
 
2020-08-08T18:45:00.842878image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length242
Median length86
Mean length84.72371736
Min length2

clean_text
Categorical

HIGH CARDINALITY
UNIFORM

Distinct count16317
Unique (%)93.5%
Missing8
Missing (%)< 0.1%
Memory size136.4 KiB
huge discount first time ever amazon history covid independence happy learning ebook amazon ebook amazon india
 
47
julycoronavirus covid status total increase confirmed cases death test hospitalisation worldwide state newyorkcity please detail supporting reports facebook link
 
39
time default bonds held china spreading covid_ covid causing trillions damage life global economy realdonaldtrump potus secpompeo trump uschina southchinasea taiwan hongkong vietnam huawei boycottchinese
 
32
corner covid experience deaths million population lower belgium spain italy sweden france netherlands ireland
 
28
heads lets minds together think nothing democrats whyre good like know help covid nasty president sorry want yall safe
 
28
Other values (16312)
17282
ValueCountFrequency (%) 
huge discount first time ever amazon history covid independence happy learning ebook amazon ebook amazon india470.3%
 
julycoronavirus covid status total increase confirmed cases death test hospitalisation worldwide state newyorkcity please detail supporting reports facebook link390.2%
 
time default bonds held china spreading covid_ covid causing trillions damage life global economy realdonaldtrump potus secpompeo trump uschina southchinasea taiwan hongkong vietnam huawei boycottchinese320.2%
 
corner covid experience deaths million population lower belgium spain italy sweden france netherlands ireland280.2%
 
heads lets minds together think nothing democrats whyre good like know help covid nasty president sorry want yall safe280.2%
 
christ since take back earth plagues pestilence assaults like covid used destroy world empire create gods kingdom earth daniel240.1%
 
something think according website covid france death rate virus death rate less wont hear fauci fake news media210.1%
 
covid measures finish world empire told showed stupid right eyes mirror events show narration tells nature remedy daniel200.1%
 
terms covid cases canadas experience million lower sweden spain iceland belgium ireland portugal italy switzerland netherlands190.1%
 
thank190.1%
 
Other values (16307)1717998.4%
 
2020-08-08T18:45:01.144030image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length251
Median length137
Mean length129.1997251
Min length3

Sentiment
Real number (ℝ)

ZEROS

Distinct count2402
Unique (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05259429118950407
Minimum-1.0
Maximum1.0
Zeros4437
Zeros (%)25.4%
Memory size136.4 KiB
2020-08-08T18:45:01.287036image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-0.4
Q1-0.05421626984
median0
Q30.2
95-th percentile0.5
Maximum1
Range2
Interquartile range (IQR)0.2542162698

Descriptive statistics

Standard deviation0.2758412401
Coefficient of variation (CV)5.24469926
Kurtosis1.815630939
Mean0.05259429119
Median Absolute Deviation (MAD)0.1333333333
Skewness0.05192925216
Sum918.5067013
Variance0.07608838973
2020-08-08T18:45:01.424881image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0443725.4%
 
0.54222.4%
 
0.24172.4%
 
0.253812.2%
 
-0.23071.8%
 
0.12961.7%
 
-0.12791.6%
 
0.42571.5%
 
-0.52411.4%
 
0.82231.3%
 
Other values (2392)1020458.4%
 
ValueCountFrequency (%) 
-1760.4%
 
-0.96< 0.1%
 
-0.91< 0.1%
 
-0.8752< 0.1%
 
-0.86666666671< 0.1%
 
ValueCountFrequency (%) 
1710.4%
 
0.93333333331< 0.1%
 
0.9251< 0.1%
 
0.9200.1%
 
0.8751< 0.1%
 

Target
Real number (ℝ)

Distinct count18
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.494457169033441
Minimum-4.0
Maximum15.0
Zeros103
Zeros (%)0.6%
Memory size136.4 KiB
2020-08-08T18:45:01.571381image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile3
Q14
median8
Q39
95-th percentile15
Maximum15
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.222535636
Coefficient of variation (CV)0.4299891991
Kurtosis0.463248983
Mean7.494457169
Median Absolute Deviation (MAD)2
Skewness0.1297645351
Sum130883.2
Variance10.38473592
2020-08-08T18:45:01.694133image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
9398222.8%
 
4260414.9%
 
7196211.2%
 
317199.8%
 
1014958.6%
 
8.712106.9%
 
811856.8%
 
1510135.8%
 
68574.9%
 
114342.5%
 
Other values (8)10035.7%
 
ValueCountFrequency (%) 
-4710.4%
 
01030.6%
 
1730.4%
 
2840.5%
 
317199.8%
 
ValueCountFrequency (%) 
1510135.8%
 
141420.8%
 
12850.5%
 
114342.5%
 
1014958.6%
 

Interactions

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2020-08-08T18:44:54.682965image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-08T18:44:55.042853image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-08T18:44:55.256759image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-08T18:44:55.409588image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-08T18:44:55.572622image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-08-08T18:45:01.863330image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-08-08T18:45:02.191422image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-08-08T18:45:02.488396image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-08-08T18:45:02.781372image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-08-08T18:44:55.991049image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-08T18:44:56.470804image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-08-08T18:44:56.751158image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

Unnamed: 0dateusernamereplies_countretweets_countlikes_countvideogeopositivedeathword_countavg_word_lengthstopwords_countchar_countstopwordsclean_textSentimentTarget
001579651200000000000realdonaldtrump946517624882250NaN22.0225.4545457141['in', 'of', 'will', 'be', 'or', 'to', 'the']making great progress davos tremendous numbers companies coming returning hottest economy jobs jobs jobs0.5666674.0
111579651200000000000realdonaldtrump864324619989600NaN22.0118.1666675109['if', 'you', 'you', 'will', 'be']sorry come immediately sent back-0.2500004.0
221579651200000000000realdonaldtrump703524342975130NaN22.0612.571429294['you', 'on']fridaybig crowd0.0000004.0
331579651200000000000realdonaldtrump343612031506050NaN22.0220.333333063[]true0.3500004.0
441579651200000000000realdonaldtrump18086198991224080NaN22.026.000000013[]pressure0.0000004.0
551579651200000000000realdonaldtrump22288103395270NaN22.0414.000000174['be']great0.8000004.0
661579651200000000000realdonaldtrump17777588364980NaN22.0612.428571293['with', 'you']great working maria0.8000004.0
771579651200000000000realdonaldtrump8460194731025750NaN22.0484.22916721250['of', 'the', 'about', 'our', 'just', 'with', 'is', 'that', 'it', 'will', 'both', 'the', 'in', 'so', 'other', 'with', 'a', 'who', 'his', 'more', 'to']many great things signed giant trade deal china bring china closer together many ways terrific working president truly loves country much come0.3333334.0
881579651200000000000realdonaldtrump21164824235720NaN22.0205.1000007121['be', 'at', 'by', 'on', 'at', 'the', 'in']interviewed eastern joesquawk cnbc world economic forum davos switzerland enjoy0.3000004.0
991579651200000000000realdonaldtrump1055921869896930NaN22.0314.35483914165['the', 'to', 'up', 'the', 'in', 'the', 'by', 'the', 'that', 'he', 'to', 'and', 'did', 'the']senates mess made house democrats biden admitted went ukraine quid stevescalise foxnews-0.1750004.0

Last rows

Unnamed: 0dateusernamereplies_countretweets_countlikes_countvideogeopositivedeathword_countavg_word_lengthstopwords_countchar_countstopwordsclean_textSentimentTarget
17454174541596672000000000000mat9450000NaN4852143151483.0445.29545514276['of', 'and', 'to', 'them', 'in', 'the', 'and', 'are', 'or', 'to', 'as', 'in', 'the', 'against']times challenge people become vulnerable seek strong proactive cohesive bipartisan government lead back recent events suggest federal state governments either unwilling unable stand together fight covid-0.1133333.0
17455174551596672000000000000allangpaterson0010NaN4852143151483.0187.1111116147['will', 'what', 'a', 'and', 'for', 'the']million covid cases today dark demeaning statistic president-0.1500003.0
17456174561596672000000000000carolynguzzi0000NaN4852143151483.0604.68333331341['in', 'is', 'all', 'a', 'and', 'after', 'the', 'all', 'over', 'with', 'the', 'will', 'not', 'be', 'or', 'it', 'will', 'be', 'as', 'a', 'we', 'have', 'a', 'for', 'and', 'can', 'this', 'in', 'and', 'it', 'is']like opposite high school political ploy election covid either existing described sara cure covid called hydroquoroquin safe0.1650003.0
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17458174581596672000000000000carman178389260000NaN4852143151483.0365.0000003216['of', 'a', 'by']thanks trump wealthy senators abandoned poor folks especially including millionaire senators cassidy kennedy rare case state ravaged twice covid today0.1200003.0
17459174591596672000000000000renterialawfirm0000NaN4852143151483.0229.2608706236['in', 'for', 'a', 'and', 'who', 'for']today rocky ride exwho doctor helped eradicate smallpox predicts covid turmoil years0.0000003.0
17460174601596672000000000000squarerootal22030NaN4852143151483.0405.17500013246['where', 'and', 'are', 'where', 'it', 'has', 'been', 'and', 'are', 'has', 'no', 'and', 'his']places covid thrives people dying places flattened deaths europe scandinavia china australia president national plan comrades like jim_jordan think thats0.0000003.0
17461174611596672000000000000miasrule1000NaN4852143151483.0314.51612912170['be', 'the', 'out', 'of', 'not', 'just', 'our', 'we', 'are', 'at', 'is', 'as']agreed lets clear taking current covid disaster fellow humans good killing violence american apple0.2000003.0
17462174621596672000000000000lindalouwhoh0000NaN4852143151483.0425.57142911278['it', 'to', 'not', 'but', 'and', 'or', 'those', 'with', 'and', 'to', 'the']comes covid resembles wealthy powerful countries instead poorer countries like brazil peru south africa large migrant populations like bahrain oman unique failure control virus0.2145243.0
17463174631596672000000000000acai_w0000NaN4852143151483.0554.78181827318['such', 'a', 'and', 'and', 'to', 'you', 'with', 'a', 'of', 'an', 'of', 'the', 'in', 'the', 'and', 'how', 'the', 'is', 'an', 'on', 'to', 'whom', 'the', 'are', 'being', 'out', 'and']bold honest statement brought integrity please give update covid spread country handling give update bailouts paid0.4666673.0